SUFI: An automated approach to spectral unmixing of fluorescent biological images

Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that uses multiple fluorescent dyes - each measuring a specific biological signal - that are simultaneously measured and subsequently “unmixed” to provide a read-out for each individual signal. This strategy allows for measuring multiple signals in a single data capture session - for example, multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are limited in scope, throughput, and availability, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images (https://github.com/LieberInstitute/SUFI). This package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using Vertex Component Analysis, and then performs one of three unmixing algorithms derived from remote sensing. We demonstrate these remote sensing algorithms’ performance on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot that is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis of smFISH data, and thereby provide a one-stop solution for multispectral fluorescence image analysis and quantification. In summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.Author summaryIn the age of rapidly advancing imaging technologies and the widespread adoption of multiplex fluorescent experiments in diverse biological models, multispectral fluorescence imaging has emerged as a powerful technique that allows researchers to observe and study several elements within a single sample – each tagged with a different fluorescent dye. Using several fluorescent probes within the same sample provides a higher level of information but leads to mixed signals. Spectral unmixing is a computational technique that can resolve these mixed signals into individual channels. Existing spectral unmixing tools solve this problem to some extent, but their availability, applicability, and throughput is limited and often requires manual intervention. In order to address this challenge, we developed a robust, flexible, and automated pipeline called SUFI (fluorescence image spectral unmixing). We demonstrate the flexibility of SUFI using four types of biological data and compare the spectral unmixing performance against widely used ZEN Black software from Zeiss. We further provide tools and resources so that the SUFI pipeline can be readily adopted by the scientific community to unmix diverse types of multiplex fluorescent biological data at scale.

Multispectral imaging technology: Visualize, analyze, phenotyping, and quantify immune cells in situ

In the personalized medicine era, the field of immunohistopathology is evolving to provide even more precise diagnostic information to efficiently apply targeting therapies. In this regard, MultiSpectral fluorescence Imaging (MSI) is a powerful and reliable technique that provides a detailed and remarkable analysis of multiple biomarkers within their histological context. In particular, the analysis of the immune infiltrate in conjunction with the expression of immune checkpoint molecules could explain why the efficacy of the promising treatments based on immune modulator monoclonal antibodies is still limited. We analyzed the advantages and the pitfalls of applying MSI technology to investigate the immune infiltrate in correlation with programmed death-ligand 1 expression in paraffin embedded ovarian cancer samples.

Multispectral Fluorescence Imaging Technique for On-Line Inspection of Fecal Residues on Poultry Carcasses

Rapid and reliable inspection of food is essential to ensure food safety, particularly in mass production and processing environments. Many studies have focused on spectral imaging for poultry inspection; however, no research has explored the use of multispectral fluorescence imaging (MFI) for on-line poultry inspection. In this study, the feasibility of MFI for on-line detection of fecal matter from the ceca, colon, duodenum, and small intestine of poultry carcasses was investigated for the first time. A multispectral line-scan fluorescence imaging system was integrated with a commercial poultry conveying system, and the images of chicken carcasses with fecal contaminants were scanned at processing line speeds of one, three, and five birds per second. To develop an optimal detection and classification algorithm to distinguish upper and lower feces-contaminated parts from skin, the principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were first performed using the spectral data of the selected regions, and then applied in spatial domain to visualize the feces-contaminated area based on binary images. Our results demonstrated that for the spectral data analysis, both the PCA and PLS-DA can distinguish the high and low feces-contaminated area from normal skin; however, the PCA analysis based on selected band ratio images (F630 nm/F600 nm) exhibited better visualization and discrimination of feces-contaminated area, compared with the PLS-DA-based developed chemical images. A color image analysis using histogram equalization, sharpening, median filter, and threshold value (1) demonstrated 78% accuracy. Thus, the MFI system can be developed utilizing the two band ratios for on-line implementation for the effective detection of fecal contamination on chicken carcasses.